This replication dataset reproduces the results in "New Analysis Reexamines The Value Of Cancer Care In The United States Compared To
Western Europe" in Health Affairs by Samir Soneji and JaeWon Yang (March 2015).  The stastical code is written in R, which is an open source statistical language.  Users may download R at http://cran.r-project.org/.  Throughout the notes, we refer to "An Analysis Of Whether Higher Health Care Spending In The United States Versus Europe Is 'Worth It’ In The Case Of Cancer" by Philipson et al as "Philipson et al".  

The cancer mortality rate data were downloaded from the World Health Organization Cancer Mortality Database on June 16, 2014.

We suggest users create the following sub-directories from their working directory for the replication: "/data", "/figures", "/tables".  We also suggest users set their working directory in R and then source the R code.  For example, suppose the working directory for the replication is named "US_Europe_Cancer" on the user's Desktop.  Then the working directory in R may be defined as "wd <- "~/Desktop/US_Europe_Cancer".  Users will also need to install the package "Amelia" in R.  Finally, set the working directory in R, "setwd(wd)".

Step 0. Set working directory in R.

Step 1. The script read.cancer.mortality.R reads cancer mortality rate data and creates an array of cancer mortality rates (age x year x sex x country x cancer).  The script also creates weighted cancer mortality rates for Western Europe.  The scripts read.cancer.mortality.amelia.R and read.cancer.mortality.ols.R create similar arrays and produce weighted mortality rates, although they vary in the replacement of missing mortality rate data.  The script read.cancer.mortality.9.select.R creates a similar array and also produces weighted cancer mortality rates for the 9 selected European countries studied by Philipson et al.

Step 2. The script calculate.stand.mx.R calculates the standardized cancer mortality rates according to the WHO 2000-2025 Standard Population (18 age-groups).

Step 3. The script calculate.deaths.averted.R calculates the number of cancer deaths averted, life-years saved, and quality-adjusted life-years saved for the US compared to Western Europe.  The other scripts calculate.deaths.averted.*.R calculate these quantities under different assumptions: amelia (multiple imputation for missing mortality rate data), ols (OLS for missing mortality rate data), post.peak (after peak mortality years), 9.select (for 9 selected European countries studied in Philipson et al) and 9.select.neg.bin (negative binomial regression model for 9 selected European countries studied in Philipson et al).

Step 4. The script eurocare_seer_cases.R calculates the percent each cancer represents of all incident cancer cases by country and used as an input into the calculation of incremental costs, life-years saved, and quality-adjusted life-years saved.  

Step 5. The script calculate.icer.R caulcuates the ratio of incremental cost and life-years saved (both total and quality-adjusted life-years saved).

Step 6. The script graph.stand.mx.R graphs the standardized mortality rates for the US and Western Europe for each cancer (exhibits 1-3 in manuscript).

